How Recommendation Engines Work
Key Points
- Recommendation engines are AI-driven systems that personalize content (videos, music, products) by analyzing user behavior patterns, and personalization can boost revenues by 5‑15% according to McKinsey.
- The global recommendation engine market is valued at roughly $6.9 billion today and is projected to triple within the next five years.
- These systems operate through five key phases: data gathering (collecting explicit data like ratings/comments and implicit data like clicks/purchases), data storage (using warehouses, lakes, or lake‑houses), analysis (applying machine‑learning algorithms to find patterns), model building, and delivering personalized suggestions.
- Even users who think they leave no trace can be profiled using demographic and psychographic data from similar users, enabling effective recommendations despite limited personal activity.
- While powerful, recommendation engines must balance benefits (increased engagement and sales) with challenges such as data privacy, algorithmic bias, and the need for robust infrastructure to handle large, diverse datasets.
Sections
- How Recommendation Engines Work - The speaker introduces recommendation engines, highlights their market growth and revenue impact, and outlines their five‑phase process—beginning with explicit and implicit data gathering—to explain how personalized suggestions are generated.
- From Storage to Collaborative Filtering - The segment outlines the flow from data storage options (warehouse, lake, lakehouse) through machine‑learning analysis and filtering stages, adds a feedback loop for continuous improvement, and introduces collaborative filtering as a primary recommendation method.
- Types of Recommendation Filtering - The passage outlines model‑based collaborative filtering using matrix factorization, content‑based filtering that relies on item attributes, and hybrid systems that merge both approaches, exemplified by Netflix’s recommendation engine.
- Challenges of Recommendation Engines - The speaker outlines the high costs, technical complexity, risk of poor or biased suggestions, and the need for quality data in recommendation systems across various industries.
Full Transcript
# How Recommendation Engines Work **Source:** [https://www.youtube.com/watch?v=gEdePRsDACc](https://www.youtube.com/watch?v=gEdePRsDACc) **Duration:** 00:10:41 ## Summary - Recommendation engines are AI-driven systems that personalize content (videos, music, products) by analyzing user behavior patterns, and personalization can boost revenues by 5‑15% according to McKinsey. - The global recommendation engine market is valued at roughly $6.9 billion today and is projected to triple within the next five years. - These systems operate through five key phases: data gathering (collecting explicit data like ratings/comments and implicit data like clicks/purchases), data storage (using warehouses, lakes, or lake‑houses), analysis (applying machine‑learning algorithms to find patterns), model building, and delivering personalized suggestions. - Even users who think they leave no trace can be profiled using demographic and psychographic data from similar users, enabling effective recommendations despite limited personal activity. - While powerful, recommendation engines must balance benefits (increased engagement and sales) with challenges such as data privacy, algorithmic bias, and the need for robust infrastructure to handle large, diverse datasets. ## Sections - [00:00:00](https://www.youtube.com/watch?v=gEdePRsDACc&t=0s) **How Recommendation Engines Work** - The speaker introduces recommendation engines, highlights their market growth and revenue impact, and outlines their five‑phase process—beginning with explicit and implicit data gathering—to explain how personalized suggestions are generated. - [00:03:04](https://www.youtube.com/watch?v=gEdePRsDACc&t=184s) **From Storage to Collaborative Filtering** - The segment outlines the flow from data storage options (warehouse, lake, lakehouse) through machine‑learning analysis and filtering stages, adds a feedback loop for continuous improvement, and introduces collaborative filtering as a primary recommendation method. - [00:06:09](https://www.youtube.com/watch?v=gEdePRsDACc&t=369s) **Types of Recommendation Filtering** - The passage outlines model‑based collaborative filtering using matrix factorization, content‑based filtering that relies on item attributes, and hybrid systems that merge both approaches, exemplified by Netflix’s recommendation engine. - [00:09:13](https://www.youtube.com/watch?v=gEdePRsDACc&t=553s) **Challenges of Recommendation Engines** - The speaker outlines the high costs, technical complexity, risk of poor or biased suggestions, and the need for quality data in recommendation systems across various industries. ## Full Transcript
I often start these videos by asking you what is what is some technical
I term or other, but I think we're all familiar with recommendation engines.
They suggest which video to watch next, which songs you might like, which products
you might be interested in, all based on using machine learning algorithms to find patterns in user behavior data
to create suggestions personalized just for you.
But do you understand how they work?
Well, let's get into it.
A recommendation engine is an AI system that suggests items to a user.
It essentially personalizes content, and that's a big deal.
So according to research by McKinsey, personalization can raise revenues.
Something like between 5 and 15%.
Now, the recommendation engine market
that's estimated to be valued today at something
like $6.88 billion,
and it's expected in the next five years to triple.
So with that in mind, let's get into how recommendation engines work.
The types of recommendation engines and the why as in
why use them in terms of benefits and challenges.
And let's start here with the how.
So to target users with suitable suggestions, a recommendation engine
typically operates in five different phases.
The first of those is called data
gathering, the data gathering phase.
Now the more we know about a given user,
the more fuel will have to guide the other four phases.
And there are two types of data that recommendation engines make use of.
Now one of those is called explicit data.
Now explicit data covers user actions
and activities like comments a user has posted online
reviews the user has written and content
the user has rated in some way.
Ratings. You know what that reminds me?
Now would be a great time
to click the thumbs up button on this video, because both I
and the YouTube recommendation engine would greatly appreciate it.
Now, the other type of data that is called implicit data,
and that's user behavior like clicks, past purchases and search history.
Now you might be thinking I never post online reviews.
I do all my web searching in incognito mode,
so recommendation engines, they won't have any data on me.
Well, maybe so, but there are other people out there
that share similar characteristics as you.
Demographics like age and psychographics, like interests and lifestyles and
recommendation engines can use this data
to personalize the content for you.
Now, after the data has been gathered,
the next step is that we need to store it somewhere.
So storage comes next.
Now that might be in a data warehouse, which can aggregate data
from different sources.
It might be a data lake which can store both structured and unstructured data,
or it might be a data lake house, which kind of combines the best of both worlds
with the data stored.
We can now move on to phase three and that is analysis.
So this is all about using machine learning
algorithms to process and examine data sets.
These algorithms detect patterns, identify correlations,
and weigh the strength of those patterns and correlations.
Once they've done that, we move into a pretty important stage,
which is the filtering stage.
Now filtering stages is filtering the data
showing the most relevant items from the previous analysis phase.
And we'll get more into filtering in just a moment.
But also, you know, like any good machine learning algorithm,
there's a fifth stage as well.
And that is the feedback
loop that we've put on the end here.
And the feedback loop regularly assesses the outputs of the recommendation system,
observes if and how the user action those recommendations,
and then uses that data to optimize the model,
hopefully enhancing its accuracy and quality over time.
Okay, so let's narrowing now on filtering.
Recommendation engines differ based on the filtering
method that they use, and there are generally three types.
Let's take a look at them and the first type is called collaborative filtering.
So let's take a look at collaborative filtering.
Now a collaborative filtering system filter suggestions
based on a particular user's likeness to others.
Now these systems assume that users with comparable
preferences will likely be interested in the same items
and potentially interact with them in similar ways in the future.
Now, actually, there are two main types of collaborative filtering systems
and one of those is memory based.
Now memory based represents users and items as a matrix.
They extend the KNN algorithm that's the k nearest neighbor algorithm,
where they aim to find their nearest neighbors in the matrix,
which can be similar users or similar items.
Now, memory based filtering can also be split down into two things.
So we've got item based
and we've got user based.
In the item based filtering, the system focuses on how users interact
with the items to find similarities between the items themselves.
So for example, if a bunch of users rate or interact
with two items in a similar way, those items are considered similar.
Now, on the other hand, user based that compares users
based on their behavior and preferences, recommending items that.
Similarly, users have liked.
Now that's memory.
The other type of collaborative filtering
that is called model based, and it uses algorithms to predict
user preferences by identifying patterns in user behavior.
And one common method is matrix factorization, where a large user item
matrix is simplified kind of squashed down into a smaller set of factors.
All right. So that's collaborative filtering.
The second type of filtering method
that is called content based.
So content based filtering
which filters recommendations based on an item's features.
So this really is all about focusing in on features.
So unlike collaborative filtering which relies on user behavior, content
based filtering looks at the specific attributes of the items themselves.
Things like keywords or product descriptions
and recommends items with similar features to those.
A user has interactive with before.
And this approach works pretty well when detailed information about
the item is available, and it's especially useful for new or niche items
that haven't really been widely rated or reviewed by users yet.
Okay, now the third
type of filtering that's simply called hybrid
hybrid filtering, which, as you probably guessed, combines
both collaborative filtering
and content based filtering, potentially overcoming some of the limitations
of each of those methods.
And a well known example of hybrid filtering
is Netflix's recommendation engine, which combines collaborative filtering
based on user ratings with content based filtering using information
like genre or actors to suggest movies or shows
All right, let's wrap this up by looking at the why.
Why do this.
What are the benefits and challenges a recommendation
engine can bring to both businesses and users?
Right.
So in the benefits column I think we need to include improved
user experience as a potential benefit here.
Recommending the right product or the service that the user wants.
Saves the user time from scrolling
endlessly through an extensive catalog.
And in fact, something like 80%
of what viewers watch on Netflix.
That comes from suggestions powered by recommendation algorithms.
Now, it can also lead to higher customer retention as well.
According to research
firm McKinsey, this enhanced customer experience.
It can translate to something like 20% higher customer satisfaction.
And if it's done well, well, ultimately
it can lead to higher revenue as well.
In fact, 35%
of what shoppers buy on Amazon comes from product recommendations.
But those are the benefits.
There are challenges as well.
Let's talk about some of those.
And one of those is there is an increase in cost.
And there's an increase in complexity.
All of that analyzing and filtering massive amounts of data.
But it requires complex architectures.
And so a significant investment in computing resources.
Another concern is what if we get bad recommendations?
Yeah, that's always a concern.
It's always a risk
if algorithms are optimized around the wrong metrics, items that are often
highly rated might be suggested more frequently than new or obscure ones,
but it might not be what the customer is actually interested in.
And we must also be concerned
about bias creeping in here as well.
Machine learning algorithms might learn societal biases present in data,
or they might learn it from human evaluators who tune the model,
resulting in inaccurate recommendations.
So that's recommendation engines.
You'll find them everywhere.
E-commerce, media and entertainment.
Travel and hospitality.
And I recommendation engine.
It's only as good as the data it's built on and the filtering method applied.
But when implemented correctly it can really transform the user experience.
And if a recommendation engine happened to lead you to
this video, well, I'd say it's working like a charm.